Applications of Machine Learning in Mobile Apps

Machine learning is being adopted far and wide in almost all the products and services right now. But most of the time we hear about it being applied to business’s backend - like Predictive Intelligence, anomaly detection on video streams, credit card fraud, etc. But Machine Learning has very interesting applications inside a mobile app as well. Here we present the practical applications of Machine Learning and Artificial Intelligence on mobile apps:

Chatbots

Chatbots are a transformational componet of any organization seeking to improve efficiency and drive innovation for both B2C and B2B businesses, on mobile as well as desktop applications. There are many benefits of chatbots brings to business, because of this businesses have been using these applications, both externally facing chatbot alongside an internal one (HR). These applications can assist 24x7, a personal assistant that can answer FAQs and features of the product, right within the app. This way, the chatbots save a tons of time & money by not requiring a manual agent, at the same time increasing customer satisfaction.

The chatbots have been remarkable in helping HR, to save close to 70% of their time spent in processing all the employee repetitive requests, and to work towards being a more strategic active entity of the business.

Product Recommendations

Based on the user preferences, content recommendations can be made to the users by usage of a recommendation engine. These preferences can be plotted based on the current content consumption behavior patterns or based on the ratings they might have given for some other content- think what Spotify is doing for music, YouTube is doing for Videos, and Medium is doing for Blogs.

Image Recognition and Tagging

If you are a phone-shutterbug then you might have faced the situation where you have thousands of funny and lovely pictures, but, at the moment you wish to share it with someone, you found it difficult to find the content (like a birthday party, trekking) of the pictures. This is where image recognition using machine learning can benefit a lot. The mobile app can identify the content of the picture in terms of activity or entities and tag them accordingly- ‘Trekking at the mountains’, ‘Birthday Party’, ‘Christmas in New York’. This means it is very easy for you to search for them, without having to manually tag each and every picture you take.

Another interesting application could be to provide more info about an entity in an image by scanning it - identify species of the dog/plant, identify the criticality of a medical condition such as a burn or skin disease, etc.

Predict User Response

Chat, email and content writing apps could assist the user with plausible responses without having to type in the whole thing. For example, an instant messenger could suggest emojis to use depending on the content and context of messages and based on the user’s preference of images. Gmail does something similar by suggesting quick replies. This is a huge time saver as the user doesn’t have to fumble with the keyboard for these quick things.

Learn and Act to User Preferences

Apps can observe user’s behavior within the app according to the time of day/ day of the week and auto-arrange new apps on the home screen or options inside an app as per usage. Basically, the apps through deep learning practices, learn “how it’s being used” from the interactions and customize itself accordingly.

Optical Character Recognition

Using the camera finder, apps could scan handwritten or printed documents, and do many things with it. It could be used to tag and make them searchable or it could be translated into other languages.

For example, apps could let users scan bills and then prepare an income-expense report by reading the content of the bills. Another use case is to let users ‘draw’ or ‘write’ on the app, and the app converts that to digital content which is more flexible for storage and retrieval.

About the Author

Aswin Kumar is the Practice Head for Mobile Solutions at V-Soft Consulting. Aswin leads the design and development that collaborates with leading companies to build mobile capabilities for existing and newly innovative platforms. Aswin and his team understand the requirement for back-end integration of cloud or premise based systems with a mobile application that delivers industry leading results for the enterprise. Aswin also leads the emerging technology initiatives like AR, AI, and ML.